The NVIDIA Deep Learning Institute (DLI), Texas A&M Institute of Data Science, Texas A&M High Performance Computing, and Texas Engineering Experiment Station invite you to attend a hands-on deep learning workshop on Feb 23rd, 2019 from 8:30AM to 5:00PM at the ILSB Auditorium exclusively for verifiable academic students, staff, and researchers.
Dr. Tao and his collaborator Dr. Fang Huang from the University of Electronic Science and Technology of China contributed a chapter on Large-Scale Remote Sensing Image Processing in the Encyclopedia of Image Processing published by the CRC Press in Nov 2018.
According to the publisher, “The Encyclopedia of Image Processing presents a vast collection of well-written articles covering image processing fundamentals (e.g. color theory, fuzzy sets, cryptography) and applications (e.g. geographic information systems, traffic analysis, forgery detection). Image processing advances have enabled many applications in healthcare, avionics, robotics, natural resource discovery, and defense, which makes this text a key asset for both academic and industrial libraries and applied scientists and engineers working in any field that utilizes image processing. Written by experts from both academia and industry, it is structured using the ACM Computing Classification System (CCS) first published in 1988, but most recently updated in 2012.”
The contribution by Dr. Tao and Dr. Fang Huang focuses on the new High Performance Computing technologies based on multiple cores, graphics processing units, Intel Many Integrated Cores, cloud computing, and big data computing platforms to process large-scale Remote Sensing images.
More information about the Encyclopedia of Image Processing can be found at Taylor & Francis web site at
Shaina D. Le is one of the TAMU team members who attended ASC18, one of three annual largest international student cluster competitions in the world.
Shaina submitted an article about her experiences at ASC18.
She will need your vote to get a surprise gift from the ASC organizer. Let’s surprise this hard-working Aggie girl!
Now, it’s your chance to vote for your favorite ASC experience report! One of the top 3 winners of our surprise gifts is to be selected from @SccTeamSegFAUlt @UniWarszawski @TAMU via twitter. And the other 2 winners via WeChat!
Read complete reports at: https://t.co/dWjF1XRhQS
— ASC HPC Challenge (@aschpc) January 17, 2019
With the support from Texas A&M Institute of Data Science, Texas Engineering Experiment Station, and Texas A&M High Performance Research Computing, Dr. Jian Tao at COE-HPC will teach a special topic course – ENGR 489 section 504 (CRN 36736) to undergraduate students on various subjects in High Performance Computing (HPC) and Big Data analytics in Spring 2019.
In this course, students will learn the fundamentals of HPC and parallel programming in a hands-on and project-based manner. In addition, they will build a functional HPC system with Raspberry Pi’s and run some benchmarks and real world applications. Each student will have access to one Raspberry Pi. After spending a week or two to get familiar with the Linux operating system and network management, students will form teams and start building smaller clusters and run some simple benchmarks. An introduction to Python programing language will be given to the students with a focus on its applications in scientific and engineering research. Students will be encouraged to practice computational thinking throughout the course. Two more weeks will be dedicated to introductory level Message Passing Interface (MPI) programming. Students shall then be able to write, compile, and test their MPI programs on their team clusters. By the end of the middle term, students will build a big cluster by linking all the Raspberry Pi’s together. In the next two weeks, students will carry out benchmark runs, discuss scheduling mechanisms to share such a cluster and learn to submit jobs managed by a queuing system. By the end of the semester, students will run some scientific and engineering applications on the cluster and carry out some performance analysis and tuning.
More about the course can be found here.
The NVIDIA Deep Learning Institute (DLI), Texas A&M Institute of Data Science, Texas A&M High Performance Research Computing, and Texas Engineering Experiment Station invite you to attend a hands-on deep learning workshop on December 14th, 2018 from 8:30AM to 5:00PM at the ILSB Auditorium exclusively for verifiable academic students, staff, and researchers.
NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning and accelerated computing.
About This Workshop:
In this hands-on course, you will learn the basics of deep learning by training and deploying neural networks. You will:
- Implement common deep learning workflows such as Image Classification and Object Detection.
- Experiment with data, training parameters, network structure, and other strategies to increase performance and capability.
- Deploy your networks to start solving real-world problems.
On completion of this course, you will be able to start solving your own problems with deep learning.
Familiarity with the basic programming, fundamentals such as functions and variables.
NVIDIA DLI Certification:
Through built-in assessments, participants can earn certification to prove subject matter competency and support professional career growth.
08:30 Registration (breakfast provided)
09:00 Deep Learning Demystified (lecture)
10:00 Image Classification with DIGITS (hands-on lab)
12:00 Lunch (provided)
13:00 Object Detection with DIGITS (hands-on lab)
14:50 Break (refreshments & soft drinks)
15:00 Neural Network Deployment with DIGITS and TensorRT (hands-on lab)
- DLI Lab #1: Image Classification with DIGITS
Learn how to leverage deep neural networks (DNN) within the deep learning workflow to solve a real-world image classification problem using NVIDIA DIGITS. You’ll walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs.
On completion of this lab, you will be able to use NVIDIA DIGITS to train a DNN on your own image classification application.
- DLI Lab #2: Object Detection with DIGITS
Many problems have established deep learning solutions, but sometimes the problem that you want to solve does not. Learn to create custom solutions through the challenge of detecting whale faces from aerial images by:
- Combining traditional computer vision with deep learning.
- Performing minor “brain surgery” on an existing neural network using the deep learning framework Caffe.
- Harnessing the knowledge of the deep learning community by identifying and using a purpose built network and end-to-end labeled data.
Upon completion of this lab, you will be able to solve custom problems with deep learning.
- DLI Lab #3: Neural Network Deployment with DIGITS and TensorRT
Deep learning allows us to map inputs to outputs that are extremely computationally intense. Learn to deploy deep learning to applications that recognize images and detect pedestrians in real time by:
- Accessing and understanding the files that make up a trained model
- Building from each function’s unique input and output
- Optimizing the most computationally intense parts of your application for different performance metrics like throughput and latency
Upon completion of this Lab, you will be able to implement deep learning to solve problems in the real world.
Workshop Setup Instructions:
1. Create an NVIDIA Developer account at http://courses.nvidia.com/join.
2. Make sure that WebSockets works for you:
- Test your laptop at http://websocketstest.com
- Under ENVIRONMENT, confirm that “WebSockets” is checked yes.
- Under WEBSOCKETS (PORT 80), confirm that “Data Receive,” “Send,” and “Echo Test” are checked yes.
3. If there are issues with WebSockets, try updating your browser. We recommend Chrome, Firefox, or Safari for an optimal performance.
4. Once onsite, visit http://courses.nvidia.com/dli-event and enter the event code provided by the instructor.
This workshop is brought to you by: NVIDIA, TAMIDS, HPRC, and TEES.
As part of the NVIDIA Deep Learning Institute (DLI) University Ambassadorship program, HPRC and College of Engineering are offering the Fundamentals of Accelerated Computing with CUDA C/C++ workshop at no cost to Texas A&M students, staff, and researchers. This intensive hands-on workshop is complementary to the existing Introduction to CUDA short course offered by HPRC.
- Time: Friday, November 9, 11:30AM-2:00PM
- Location: SCC 102.B
- Prerequisites: Experience with C or C++
The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. Experience C/C++ application acceleration by:
- Accelerating CPU-only applications to run their latent parallelism on GPUs
- Utilizing essential CUDA memory management techniques to optimize accelerated applications
- Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
- Leveraging command line and visual profiling to guide and check your work
- Upon completion, you’ll be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You’ll understand an iterative style of CUDA development that will allow you to ship accelerated applications quickly.
As the only team from Texas, the Texas A&M Student Cluster Competition Team with 6 engineering undergraduates is selected, together with 14 other teams worldwide, to participate the Student Cluster Competition at 2018 Supercomputing Conference at Dallas this November.
The Student Cluster Competition is an HPC multi-disciplinary experience integrated within the HPC community’s biggest gathering, the Supercomputing Conference. The competition is a microcosm of a modern HPC center that teaches and inspires students to pursue careers in the field. It demonstrates the breadth of skills, technologies and science that it takes to build, maintain and utilize supercomputers. In this real-time, non-stop, 48-hour challenge, teams of undergraduate and/or high school students assemble small clusters on the exhibit floor, and race to complete real-world workloads across a series of applications and impress HPC-industry judges
More information about the Student Cluster Competition at 2018 Supercomputing Conference at Dallas can be found at
Dr. Jian Tao has been granted NVIDIA Deep Learning Institute (DLI) University Ambassadorship. DLI Ambassadors are a select group of DLI Certified Instructors committed to teach DLI workshops and host DLI lab meetups at universities, academic conferences, and events at no cost to attendees (academic students, staff, and researchers only). The COE HPC team will be leveraging the resources and opportunities offered in this academic-industry collaboration to promote HPC education for both undergraduate and graduate students at A&M Engineering. We will offer training opportunities for research staff and faculty members to catch up with the latest computing technologies with the support from NVIDIA.
The following is a basic list of the benefits and Ambassador contributions of the program.
- Free DLI Instructor Certification ($1000 value)
- Ability to bring free, world-class DL training to your academic community using proven DLI cloud-based training platform and lecture materials (avg. $2000 compute value per workshop)
- Formal badge recognition for DLI University Ambassador status
- Reimbursement for catering and travel expenses up to $500 per hosted DLI event (reimbursement requests submitted with receipts).
- Access to the DLI Ambassador Event Kit for best practices guides, “Train-the-Trainer” assets, event promotion assets, and workshop slide deck content
- Early access to new DLI Teaching Kits and free cloud-based GPU platforms for students in curriculum courses
How do you compete in supercomputing? The students on the Texas A&M University Supercomputing Team know that’s going to be the first question before it’s even asked.
“Build. Run. Optimize,” said Dr. Jian Tao, advisor for the team and Texas A&M Engineering Experiment Station researcher. “You build the computer, you run programs on it and then you optimize the performance.”
They’re simple words, but the competition is anything but. Over 300 student teams from around the world entered the Asia Supercomputer Community’s Student Supercomputer Challenge (ASC18), but only 20 made the cut to the finals to be held in May in China. Texas A&M’s group is not only the lone team from the United States to make the finals, it’s the only team from the entire Western Hemisphere.
More about the story can be found at TEES News
Starting from 2018, COE-HPC is leading an AggiE_Challenge project aiming to encourage undergraduate students to learn and participate research activities on high performance computing. Our project is open to students from freshman to senior level. Undergraduate students who are interested to participate can enroll in an ENGR – 491 section and receive course credit (1-4 credit hours). More about the course can be found at https://coehpc.engr.tamu.edu/aggie_challenge.